ODSC - Open Data Science

SEPTEMBER 27, 2023

In this article, we will delve into the concept of data lakes, explore their differences from data warehouses and relational databases, and discuss the significance of data version control in the context of large-scale data management. Schema Enforcement: Data warehouses use a “schema-on-write” approach.

Data Lakes 98

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Alation

APRIL 25, 2023

It’d be difficult to exaggerate the importance of data in today’s global marketplace, especially for firms which are going through digital transformation (DT). Using bad data, or the incorrect data can generate devastating results. It can also help you gain key insights so you can make the most out of the data you have.

Data Governance 40

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IBM Journey to AI blog

APRIL 25, 2023

To do so, Presto and Spark need to readily work with existing and modern data warehouse infrastructures. Now, let’s chat about why data warehouse optimization is a key value of a data lakehouse strategy. To effectively use raw data, it often needs to be curated within a data warehouse.

Data Warehouse 58

Data Warehouse Data Engineer Data Engineering Data Engineering 58

Tableau

MAY 11, 2022

Data fabrics are gaining momentum as the data management design for today’s challenging data ecosystems. At their most basic level, data fabrics leverage artificial intelligence and machine learning to unify and securely manage disparate data sources without migrating them to a centralized location.

Tableau 98

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Tableau

MAY 11, 2022

Data fabrics are gaining momentum as the data management design for today’s challenging data ecosystems. At their most basic level, data fabrics leverage artificial intelligence and machine learning to unify and securely manage disparate data sources without migrating them to a centralized location.

Tableau 98

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IBM Journey to AI blog

JULY 24, 2024

Optimizing performance with fit-for-purpose query engines In the realm of data management, the diverse nature of data workloads demands a flexible approach to query processing. The integration with established data warehouse engines ensures compatibility with existing systems and workflows.

Data Governance 69

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IBM Journey to AI blog

MAY 9, 2023

Watsonx.data will allow users to access their data through a single point of entry and run multiple fit-for-purpose query engines across IT environments. Through workload optimization an organization can reduce data warehouse costs by up to 50 percent by augmenting with this solution. [1]

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IBM Journey to AI blog

OCTOBER 12, 2023

Accounting for the complexities of the AI lifecycle Unfortunately, typical data storage and data governance tools fall short in the AI arena when it comes to helping an organization perform the tasks that underline efficient and responsible AI lifecycle management. And that makes sense.

AI 93

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Pickl AI

OCTOBER 17, 2024

Introduction ETL plays a crucial role in Data Management. This process enables organisations to gather data from various sources, transform it into a usable format, and load it into data warehouses or databases for analysis. Loading The transformed data is loaded into the target destination, such as a data warehouse.

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IBM Journey to AI blog

OCTOBER 30, 2023

Thus, DB2 PureScale on AWS equips this insurance company to innovate and make data-driven decisions rapidly, maintaining a competitive edge in a saturated market. The platform provides an intelligent, self-service data ecosystem that enhances data governance, quality and usability.

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Alation

JANUARY 17, 2023

The modern data stack is a combination of various software tools used to collect, process, and store data on a well-integrated cloud-based data platform. It is known to have benefits in handling data due to its robustness, speed, and scalability. A typical modern data stack consists of the following: A data warehouse.

Data Warehouse 52

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IBM Journey to AI blog

AUGUST 4, 2023

Data democratization instead refers to the simplification of all processes related to data, from storage architecture to data management to data security. It also requires an organization-wide data governance approach, from adopting new types of employee training to creating new policies for data storage.

Data Lakes 73

Data Lakes AI AI Data Governance 73

Alation

MAY 16, 2023

This makes it easier to compare and contrast information and provides organizations with a unified view of their data. Machine Learning Data pipelines feed all the necessary data into machine learning algorithms, thereby making this branch of Artificial Intelligence (AI) possible.

Data Pipeline 69

Data Pipeline Data Governance Data Lakes Data Warehouse 69

ODSC - Open Data Science

JUNE 12, 2023

They all agree that a Datamart is a subject-oriented subset of a data warehouse focusing on a particular business unit, department, subject area, or business functionality. The Datamart’s data is usually stored in databases containing a moving frame required for data analysis, not the full history of data.

Power BI 52

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IBM Journey to AI blog

JULY 5, 2023

Their fast adoption meant that customers soon lost track of what ended up in the data lake. And, just as challenging, they could not tell where the data came from, how it had been ingested nor how it had been transformed in the process. Data governance remains an unexplored frontier for this technology.

Data Lakes 78

Data Lakes Data Warehouse Data Governance Analytics 78

Alation

AUGUST 26, 2021

It asks much larger questions, which flesh out an organization’s relationship with data: Why do we have data? Why keep data at all? Answering these questions can improve operational efficiencies and inform a number of data intelligence use cases, which include data governance, self-service analytics, and more.

Data Governance 64

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phData

JULY 18, 2023

With the birth of cloud data warehouses, data applications, and generative AI , processing large volumes of data faster and cheaper is more approachable and desired than ever. First up, let’s dive into the foundation of every Modern Data Stack, a cloud-based data warehouse.

Data Warehouse 52

Data Warehouse Analytics Analytics Cloud Data 52

Precisely

MAY 13, 2024

If you’re in the market for a data integration solution, there are many things to consider – including the flexibility of integration solutions, the availability of a strong network of service providers, and the vendor’s reputation for thought leadership in the integration space.

Data Governance 52

Data Governance Data Pipeline Cloud Data Data Quality 52

Pickl AI

OCTOBER 17, 2024

It is a data integration process that involves extracting data from various sources, transforming it into a suitable format, and loading it into a target system, typically a data warehouse. ETL is the backbone of effective data management, ensuring organisations can leverage their data for informed decision-making.

ETL 52

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IBM Journey to AI blog

JULY 3, 2024

Multiple data applications and formats make it harder for organizations to access, govern, manage and use all their data for AI effectively. Scaling data and AI with technology, people and processes Enabling data as a differentiator for AI requires a balance of technology, people and processes.

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IBM Journey to AI blog

MARCH 14, 2024

Businesses face significant hurdles when preparing data for artificial intelligence (AI) applications. The existence of data silos and duplication, alongside apprehensions regarding data quality, presents a multifaceted environment for organizations to manage.

AWS 93

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Pickl AI

AUGUST 9, 2024

Data Warehousing Solutions Tools like Amazon Redshift, Google BigQuery, and Snowflake enable organisations to store and analyse large volumes of data efficiently. Students should learn about the architecture of data warehouses and how they differ from traditional databases.

Big Data 52

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Alation

MAY 16, 2023

This makes it easier to compare and contrast information and provides organizations with a unified view of their data. Machine Learning Data pipelines feed all the necessary data into machine learning algorithms, thereby making this branch of Artificial Intelligence (AI) possible.

Data Pipeline 40

Data Pipeline Data Governance Data Lakes Data Warehouse 40

Pickl AI

OCTOBER 15, 2024

ETL (Extract, Transform, Load) is a core process in data integration that involves extracting data from various sources, transforming it into a usable format, and loading it into a target system, such as a data warehouse. It automatically discovers and catalogues data, making it easier to prepare it for analytics.

ETL 52

ETL Azure AWS Data Governance 52

phData

APRIL 18, 2023

Snowflake enables organizations to instantaneously scale to meet SLAs with timely delivery of regulatory obligations like SEC Filings, MiFID II, Dodd-Frank, FRTB, or Basel III—all with a single copy of data enabled by data sharing capabilities across various internal departments.

Data Silos 52

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Alation

FEBRUARY 13, 2020

Earlier this month in London, more than 1,600 data and analytics leaders and professionals gathered for the Gartner Data & Analytics Summit. It was probably a surprise to no one that artificial intelligence (AI) took center stage.

Machine Learning 52

Machine Learning Machine Learning Analytics Analytics 52

Pickl AI

APRIL 26, 2024

The mode is the value that appears most frequently in a data set. Machine learning is a subset of artificial intelligence that enables computers to learn from data and improve over time without being explicitly programmed. Data Warehousing and ETL Processes What is a data warehouse, and why is it important?

Data Analyst 52

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Pickl AI

JULY 25, 2023

Together, data engineers, data scientists, and machine learning engineers form a cohesive team that drives innovation and success in data analytics and artificial intelligence. Their collective efforts are indispensable for organizations seeking to harness data’s full potential and achieve business growth.

Data Engineering 52

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Alation

FEBRUARY 13, 2020

In his book titled “The Fourth Industrial Revolution,” Klaus Schwab describes the age as, “characterized by a much more ubiquitous and mobile internet, by smaller and more powerful sensors that have become cheaper, and by artificial intelligence and machine learning.” Artificial intelligence without human collaboration fails.

Machine Learning 52

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Dataversity

APRIL 30, 2021

The goal of digital transformation remains the same as ever – to become more data-driven. We have learned how to gain a competitive advantage by capturing business events in data. Events are data snap-shots of complex activity sourced from the web, customer systems, ERP transactions, social media, […].

Data Pipeline 98

Data Pipeline Data Warehouse Data Governance Data Quality 98

ODSC - Open Data Science

JANUARY 18, 2024

This means that not only do the proper infrastructures need to be created, and maintained, but data engineers will be at the forefront of data governance and access to ensure that no outside actors or black hats gain access which could spell compliance doom for any company.

Data Engineering 52

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Alation

OCTOBER 21, 2021

But, on the back end, data lakes give businesses a common repository to collect and store data, streamlined usage from a single source, and access to the raw data necessary for today’s advanced analytics and artificial intelligence (AI) needs. Irrelevant data. Ungoverned data.

Data Lakes 52

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Precisely

APRIL 15, 2025

Leaders must act now Addressing skills gaps, investing in dedicated tools, and aligning governance practices are critical steps to ensure AI success and mitigate risk. Artificial intelligence (AI) and machine learning (ML) are transforming businesses at an unprecedented pace.

Data Observability 69

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